Page 24 - Monocle Quarterly Journal Vol 1 Issue 1 Q4

BANKING
“Recall that a bank in its liability structure to meet its asset demand will depend on either deposits made by corporate or individual customers or on the interbank market.”
Banks themselves, therefore, experienced previously unheard of default frequencies comparable only to the defaults that occurred post 1929.
Using Fundamental Ratios as Predictive Factors
e second area of interest was the ratios themselves. It was decided by the Monocle Research Team to turn towards the fundamental analysis concept from the beginning. In 1966, William H. Beaver used nancial ratios with a univariate technique to predict Financial Distress. He classi ed Financial Distress as bankruptcy, insolvency, liquidation for the bene t of a creditor, rms which defaulted on loan obligations or rms that missed preferred dividend payments.
Beaver’s technique accurately classi ed 78 percent of the sample ‘dis- tressed’ banks up to ve years prior to failure. His research concluded that the cash ow to debt ratio was the single best indicator of bankruptcy. To overcome many of the inconsistencies found in Beaver’s research, in 1968, Edward I Altman improved on Beaver’s univariate model by introducing a multiple discriminant approach. His results found that ve nancial ratios were signi cant predictors in the nancial distress prediction model. ese ratios are: working capital to total assets, retained earnings to total assets, earnings before interest and taxes to total assets, market value equity to par value of debt and sales to total assets.
Banks and nancial institutions in particular are generally more highly leveraged than the industrial institutions Beaver and Altman studied. We could not therefore make use of the same ratios or the same co- e cients of such ratios to create anything like the Altman z-score. In order to create our own database and to conduct the necessary analysis, the income statements and balance sheets of a sample of banks were extracted, constructed and normalised into a single common format. e sample of banks consisted of 20 Financially Distressed banks selected from the top 1000 banks according to size and demographics and 20 Non-Financially Distressed banks randomly chosen from the top 60 Non-Financially Distressed banks on the Banker List. It is essential to note that the selection process was not perfectly scienti c and was based on our desire to observe outcomes in di erent demographic regions of the world. Our sample size of a total of 40 banks was also small owing to the time constraints involved in normalising banks’ income statements and balance sheets into a single common format. Further research is currently underway within our research team and these should be noted
to be preliminary results.
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